Time efficiency analysis for undersampled quantitative MRI acquisitions

To realize Quantitative MRI (QMRI) with clinically acceptable scan time, acceleration factors achieved by conventional parallel imaging techniques are often inadequate. Further acceleration is possible using model-based reconstruction. We propose a theoretical metric called TEUSQA: Time Efficiency for UnderSampled QMRI Acquisitions to inform sequence design and sample pattern optimisation. TEUSQA is designed for a particular class of reconstruction techniques that directly estimate tissue parameters, possibly using prior information to regularize the estimation. TEUSQA can be used to evaluate undersampling patterns for multi-contrast QMRI sequences targeting any tissue parameter. To verify the time efficiency predicted by TEUSQA, we performed Monte Carlo simulations and an accelerated parameter mapping with two sequences (Inversion prepared fast spin echo for $T_1$ and $T_2$ mapping and 3D GRASE for $T_2$ and B0 inhomogeneity mapping). Using TEUSQA, we assessed several ways to generate undersampling patterns in silico, providing insight into the relation between sample distribution and time efficiency for different acceleration factors. The time efficiency predicted by TEUSQA was within 15% of that observed in the Monte Carlo simulations and the prospective acquisition experiment. The assessment of undersampling patterns showed that a class of good patterns could be obtained by low-discrepancy sampling. We believe that TEUSQA offers a valuable instrument for developers of novel QMRI sequences pushing the boundaries of acceleration to achieve clinically feasible protocols. Finally, we applied a time-efficient undersampling pattern selected using TEUSQA for a 32-fold accelerated scan to map $T_1$ & $T_2$ mapping of a healthy volunteer.     

Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping

Recent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-scale clinical applications. However, inaccuracy from automated methods could lead to incorrect conclusions, diagnoses or even harm to patients. Manual inspection for potential inaccuracies is labor-intensive and time-consuming, hampering progress towards fast and accurate clinical reporting in high volumes. To promote reliable fully-automated image analysis, we propose a quality control-driven (QCD) segmentation framework. It is an ensemble of neural networks that integrate image analysis and quality control. The novelty of this framework is the selection of the most optimal segmentation based on predicted segmentation accuracy, on-the-fly. Additionally, this framework visualizes segmentation agreement to provide traceability of the quality control process. In this work, we demonstrated the utility of the framework in cardiovascular magnetic resonance T1-mapping - a quantitative technique for myocardial tissue characterization. The framework achieved near-perfect agreement with expert image analysts in estimating myocardial T1 value ($r=0.987,$ $p<.0005$; mean absolute error (MAE)=11.3ms), with accurate segmentation quality prediction (Dice coefficient prediction MAE=0.0339) and classification (accuracy=0.99), and a fast average processing time of 0.39 second/image. In summary, the QCD framework can generate high-throughput automated image analysis with speed and accuracy that is highly desirable for large-scale clinical applications.     

Von Mises Strain as a Risk Marker for Vulnerability of Carotid Plaque: Preliminary Clinical Evaluation of Cerebral Infarction

Non-invasive assessment of carotid artery plaque vulnerability is a key issue for cerebrovascular disease. This study investigates Von Mises strain imaging in patients by relating Von Mises strain to cerebral infarction presentation. Ultrasonography was performed in patients evaluated for carotid artery stenosis. Strains were estimated by a flow-driven diffusion method and least-squares regression applying Kalman filtering. Von Mises strains ${?}_{\text{VM}}^{\text{sys}}$ and ${?}_{\text{VM}}^{\text{dia}}$ were calculated by averaging four or five cardiac cycles in systole and diastole, respectively. Von Mises strain (peak, coefficient of variance, skewness and kurtosis) in patients with cerebral infarction was compared with that in the control group. Higher Von Mises peak strain localized to echolucent areas on B-mode imaging. Higher peak strain was found in patients with cerebral infarction compared with the control group (p?=?0.02 for ${?}_{\text{VM}}^{\text{dia}}$ and p?=?0.001 for ${?}_{\text{VM}}^{\text{sys}}$). The area under the receiver operating characteristic curve for peak ${?}_{\text{VM}}^{\text{sys}}$ was 0.761 (p?=?0.001) with high sensitivity and specificity. Peak strain also correlated with homocysteine (r?=?0.345, p?=?0.007, for ${?}_{\text{VM}}^{\text{dia}}$; r?=?0.287, p?=?0.036, for ${?}_{\text{VM}}^{\text{sys}}$) and hypersensitive C-reactive protein (r?=?0.399, p?=?0.043, for ${?}_{\text{VM}}^{\text{dia}}$; r?=?0.195, p?=?0.034, for ${?}_{\text{VM}}^{\text{sys}}$) levels. The coefficient of variance, skewness and kurtosis of ${?}_{\text{VM}}^{\text{dia}}$ or ${?}_{\text{VM}}^{\text{sys}}$ were also associated with homocysteine levels. In conclusion, this study indicates that peak Von Mises strain is a potential clinical risk marker for carotid plaque vulnerability and cerebral infarction.